Through action planning, a machine can be taught anything that a programmer or developer wants it to learn with goal-driven learning.
Whitepaper: How Companies Can Start Using Artificial Intelligence
Building out the artificial intelligence models for a fully enriched goal-driven system will vary widely, but typically follows the approach outlined below, in a highly serial sequence. Goal-driven systems rely on an action plan or an objective to achieve a specific set of goals. Establishing a sequence of actions and learning through trial and error is how a goal system operates. Goal-driven systems have been deployed to beat some of the world’s best players in games like chess, Go, and DOTA 2.
A goal-driven system must go through an initial planning stage. This stage defines preconditions and how a particular sequence of actions will run. These actions will go through trial and error through machine learning, creating neural pathways along the way and eventually meeting the original goal or intended purpose for a goal-driven system. In Artificial Intelligence, a world or environment exists. Within that world exists an agent, which could be a robot or entity. The agent can make decisions based on learned preconditions, utilizing the best possible outcomes through trial and error in its environment. There are four overarching machine learning systems that are goal-driven.
Many use the game of chess to describe this particular system for a reason. Reactive AI cannot react to anything beyond what it was coded to do through machine learning and trial and error. A reactive AI system usually has a bounding box and a set of specific conditions. Many use the game of chess to describe this particular system for a reason. Machine learning channels through each possible move through the use of trial and error. Thus they are bound by the moves it is trained to learn. Another example would be an email spam filter. The system was preconditioned to only protect email and vet out bots and nothing more. A preconditioned reactive artificial system cannot make decisions because the architecture was designed to keep the specific system conditioned to stay within the parameters of its environment. As a result, reactive AI does not have the ability to interpret consumer traffic or handle real-world scenarios. Using a reactive AI system in certain instances is necessary as this type of system works well as a load balancer, managing the flow of heavy traffic, and avoiding latency and breakdowns during high peaks.
Limited Memory AI is primarily where the focus is for many developers right now. Limited Memory AI systems are capable of storing past data and predictions. Those predictions then expand upon the previous predictions. An example of this would be the Alexa hosting music software such as Pandora or Spotify. When a user tells Alexa to play a specific song or podcast, the system will take a quick snapshot of that request and store it for later. That information will then be used to predict what type of songs or podcasts the user would appreciate based on the previous requests and similarities. There are three types of models that create limited memory systems and can learn to make decisions based on trial and error.
Artificial emotional intelligence is a theoretical system in which artificial intelligence can interact with human thought and interpret emotion. This system effectively analyzes thousands of verbal, physical, and social cues such as facial expressions. Theory of mind in AI means training a machine to understand and process everything humans feel and predict future behavior. This of course has to begin at the most basic level. A baby does not get up and start walking from day one and neither can an artificial emotional intelligence.
Self-aware systems are also theoretical and are a type of AI that can only be imagined. It is believed that in the distant future, systems will have been so well trained and in tune with human emotion due to the Theory of the mind coming into play on top of all the Limited Memory training. Allegedly, the world will reach a point where AI can self-actualize, be actively intelligent and regulate their thoughts. This goal system may seem like it is in the far-in-the-distance future but it may not be that far off. If Artificial emotional intelligence can process the theory of mind, it only seems natural that AI can process basic thoughts.
"Clients are taking on new models for ai that they've never really considered before."
Russ Langel, Enterprise Cloud Architect, Product Perfect
"Artificial intelligence machine learning models can be very effective - you just have to take baby steps to engage this area."
Shawn Livermore, Senior Consultant, Product Perfect
"We've positioned ourselves for artificial intelligence with models and algorithms and overall a better playbook for our clients."
Jeff Sherwood, Senior Product Design Consultant, Product Perfect
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How Companies Can Start Using Artificial Intelligence
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